Joint modeling, covariate adjustment, and interaction: Contrasting notions in risk prediction models and risk prediction performance

Epidemiology

Volumn 22, Issue 6, 2011, Pages 805-812

  Kerr, Kathleen F ^a   Pepe, Margaret S ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; KIDNEY ARTERY STENOSIS; PREDICTION; PREDICTOR VARIABLE; PRIORITY JOURNAL; PROSTATE CANCER; RISK FACTOR; STATISTICAL MODEL;

BIOLOGICAL MARKERS; DISEASE; EPIDEMIOLOGIC METHODS; HUMANS; MALE; MODELS, STATISTICAL; PROSTATIC NEOPLASMS; RENAL ARTERY OBSTRUCTION; RISK ASSESSMENT; RISK FACTORS; ROC CURVE;

EID: 80053608699     PISSN: 10443983     EISSN: 15315487     Source Type: Journal    
DOI: 10.1097/EDE.0b013e31823035fb     Document Type: Article

References (21)

1. Janes H, Pepe MS. Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am J Epidemiol. 2008;168:89-97. (Pubitemid 351881892)
2. Gail MH. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst. 2008;100:1037-1041. (Pubitemid 352019794)
3. Wacholder S, Hartge P, Prentice R, et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med. 2010;362: 986-993.
4. Green DM, Swets J. Signal Detection Theory and Psychophysics. New York: Wiley; 1966.
5. Egan JP. Signal Detection Theory and ROC Analysis. New York: Academic Press; 1975.
6. McIntosh MW, Pepe MS. Combining several screening tests: optimality of the risk score. Biometrics. 2002;58:657-664. (Pubitemid 35001484)
7. Huang Y, Pepe MS. A parametric ROC model based approach for evaluating the predictiveness of continuous markers in case-control studies. Biometrics. 2009;65:1133-1144.
8. Janes H, Pepe MS. Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve. Biometrika. 2009;96:371-382.
9. McKnight B. Effect modification. In: Armitage P, Colton T, eds. Encyclopedia of Biostatistics. New York: John Wiley and Sons; 1998.
10. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. New York: Oxford University Press; 2003.
11. Pepe MS, Kerr KF, Longton G, Wang Z. Testing for improvement in prediction model performance. Technical Report 379. Available at: www.bepress.com/uwbiostat/paper379.
12. Deras IL, Aubin SMJ, Blase A, et al. PCA3-a molecular urine assay for predicting prostate biopsy outcome. J Urol. 2008;179:1587-1592.
13. Janssens ACJW, Deng Y, Borsboom GJ, Eijkemans MJ, Habbema JDF, Steyerberg EW. A new logistic regression approach for the evaluation of diagnostic test results. Med Decis Making. 2005;25:168-177. (Pubitemid 40411459)
14. Pepe MS. Three approaches to regression analysis of receiver operating characteristic curves for continuous test results. Biometrics. 2008;54: 124-135.
15. Janes H, Longton G, Pepe MS. Accommodating covariates in receiver operating characteristic analysis. STATA J. 2009;9:17-39.
16. Cai T, Pepe MS. Semiparametric receiver operating characteristic analysis to evaluate biomarkers for disease. J Am Stat Assoc. 2002;97:1099-1107. (Pubitemid 36136569)
17. Janes H, Pepe MS. Matching in studies of classification accuracy: implications for analysis, efficiency, and assessment of incremental value. Biometrics. 2008;64:1-9. (Pubitemid 351316856)
18. Gail MH, Pfeiffer RM. On criteria for evaluating models of absolute risk. Biostatistics. 2005;6:227-239. (Pubitemid 41240355)
19. Pepe MS, Feng Z, Huang Y, et al. Integrating the predictiveness of a marker with its performance as a classifier. Am J Epidemiol. 2008;167: 362-368. (Pubitemid 351197592)
20. Gu W, Pepe MS. Measures to summarize and compare the predictive capacity of markers. Int J Biostat. 2009;5:Article 27.
21. Pepe M, Gu J, Morris DE. The potential of genes and other markers to inform about risk. Cancer Epidemiol Biomarkers Prev. 2010;19:655-665.